A significant need exists for early prediction of therapeutic response in Renal Cell Carcinoma (RCC), which comprises over 90% of kidney cancer. Targeted therapies (TT) inhibiting angiogenesis can be highly effective, yet evaluation of therapy efficacy is limited. The standard of care in evaluation of therapeutic efficacy is to measure tumor size after 12 weeks of therapy. While awaiting their 12 week evaluation, up to 20% of patients do not respond to therapy, enduring side effects of the therapies together with tumor progression. We aim to identify response to therapy in the early treatment phase. We will perform a prospective clinical study that combines novel magnetic separation techniques with new nanoproteomic technology to measure biological predictors of early response to therapy in RCC. If successful, this work would radically shift the current treatment and monitoring paradigm of cancer patients. The proposed project is organized into two specific aims: Aim 1: Develop magnetic sifter technology to isolate CTCs from patients with RCC, based on CAIX surface protein expression. Aim 2: Profile RCC CTCs and host immune response to predict response to TT. The utility of the project includes: 1) Combined quantitative analysis of circulating tumor cells (CTCs) and host immune cells will allow accelerated prediction of clinical response within two weeks of TT. 2) Early quantitative analysis of biologic response will reduce unnecessary toxicity as well as provide an early opportunity to switch to another active agent before significant progression occurs. Our work will allow physicians to individually tailor the us of targeted therapeutics by measuring early response to treatment. Additionally, the magnetic sifter technology is being developed for enriching circulating tumor cells (CTC) in other solid tumors for personalized cancer therapy. Therefore, the proposed technology and clinical studies represent a substantial advance over the standard care and has great transformative potential in multiple areas of circulating tumor cells, personalized medicine, cancer biology, and translational molecular diagnostics.